When I was learning science in high school, I was mesmerized by the notion that scientific facts were true, myths were false, and there were still things that needed to be „figured out“. I was very impressed by the way computers were all about 1’s and 0’s (it wasn’t until much later that I learned computers didn’t actually divide truth and falsehood quite that neatly). Several years ago, I made a graphic image that shows the difference between the way it appears that humans think vs. the way it appears that computers think.

Note that I didn’t label which side represents human thinking vs. computer thinking. What we usually experience when we use computers is either TRUE or FALSE – we are not normally aware that there is actually a „DON’T KNOW“ state in between those two extremes. About a decade ago, I was very adamant about three-state logics.

Several decades ago, when I was just embarking on dissertation research (which was never finished, but that story is beyond the scope of this article), I was very adamant about something called „modal logic“ – a field in philosophy (and linguistics) which focuses on human modes of thought (such as „knowing“ vs. „believing“). Since humans often make references to such modes, I was hoping to unlock a hidden treasure behind such concepts. Yet they remain elusive to me to this day, even though I may quite often be heard to utter something like „I think…“ or „I believe…“ or indeed many such modes (usually using so-called „modal verbs“).

I think the less room we allow for such modalities – the smaller the amount of space we make for cases in which we acknowledge that we really don’t know, the more likely we are to make mistakes / errors.

Statisticians might be very cool to acknowledge „type 1“ and „type 2“ errors without even batting an eyelash, but for most regular folks it makes a world of difference whether we want X, whether we fear Y, whether we hope or wish or whatever.

Such very human modes of thought are rampant in our everyday lives and thinking, yet they are not given very much (or even any) room in the computer world. When there is no room whatsoever for „maybe“, then I predict the algorithms processing the data will probably be wrong.

There are virtually innumerable fans of so-called „big data“. Countless fanatics of this quasi-scientific method will swear on a stack of bibles that if you count anything – it really doesn’t matter what, as that minute detail will certainly „emerge“ from the data itself – you will be rewarded with insights beyond your wildest dreams. Such descendents of bean-counters from previous centuries have moved on to grains of sand, dust particles, the colors of a beautiful sunset, whatever.

These people may strongly believe in science – without actually understanding much about scientific methods.

There seems to be a link between such lacking understanding and fanaticism. Let’s go back to one of the greatest leaders of fanatical movements ever: Adolf Hitler was probably one of the most (if not even the most) quintessial dictators of all times. I think what many people overlook, though, in this example is not that he was able to mesmerize such humungous masses, but rather how the masses let themselves become mesmerized.

Fans follow leaders (perhaps they should instead watch the parking meters ). There is a sort of quirky rationality to this behavior: When fans follow their leader, they apparently feel they no longer have to think themselves… – they simply accept whatever their leader says (i.e., dictates). This saves energy, because thinking can be quite difficult. Not thinking is easier than thinking.

The important takeaway is this: If people feel able to let someone else do the thinking, they seem very willing to do so. One way they feel able to enable a dictator to think for them is if / when other people seem to approve of the dictator. Other people’s approval of a dictator seems to make it „OK“ to let the dictator do as he / she pleases… – whether the dictator is a politician, a celebrity, a brand name, or anything anyone happens to be a fan (i.e., a fanatical follower) of.

When popular brand names such as Google or Facebook sell „big data“, of course they tell naive and innocent consumers a story about how important big data is in order for consumers to be able to find leaders. What they don’t tell such consumers (as those people who are willing to believe this story) is that the „big data“ plans are actually all about tracking consumer behavior. What they don’t tell advertisers is that the consumer behavior they track actually isn’t actually a pot of gold at the end of a rainbow, but merely a fanatical delusion hardly worth any more than a single grain of sand.